gams/cplex 6.5 user notes

GAMS/Cplex 6.5 User Notes

Table of Contents

Introduction How to Run a Model with Cplex Overview of Cplex

Linear Programming Mixed-Integer Programming

GAMS Options Summary of Cplex Options

Preprocessing and General Options Simplex Algorithmic Options Simplex Limit Options Simplex Tolerance Options Barrier Specific Options MIP Algorithmic Options MIP Limit Options MIP Tolerance Options Output Options Example of a GAMS/Cplex Options File

Special Notes Physical Memory Limitations Using Special Ordered Sets Running Out of Memory for MIP problems Failing to Prove Integer Optimality

GAMS/Cplex Log file Detailed description of Cplex Options Appendix: Cplex Licensing

PC Licensing Unix Licensing Licensing Commands Installing a New License Updating an Existing License

1 Introduction

GAMS/Cplex is a GAMS solver that allows users to combine the high level modeling capabilities ofGAMS with the power of Cplex optimizers. Cplex optimizers are designed to solve large, difficultproblems quickly and with minimal user intervention. Access is provided (subject to proper licensing) toCplex solution algorithms including the Linear Optimizer and the Barrier and Mixed Integer Solvers.While numerous solving options are available, GAMS/Cplex automatically calculates and sets mostoptions at the best values for specific problems.

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All Cplex options available through GAMS/Cplex are summarized at the end of this document.

2 How to run a model with Cplex

The following statement can be used inside your GAMS program to specify using Cplex

Option LP = Cplex ; { or RMIP or MIP }

The above statement should appear before the Solve statement. The MIP capability is separatelylicensed, so you may not be able to use Cplex for MIP problems on your system. If Cplex was specifiedas the default solver during GAMS installation, the above statement is not necessary.

3 Overview of Cplex

3.1 Linear Programming

Cplex solves LP problems using several alternative algorithms. The majority of LP problems solve bestusing Cplex's state of the art modified primal simplex algorithm. Certain types of problems benefit fromusing the alternative dual simplex algorithm, the network optimizer, or the barrier algorithm. Thealgorithm that Cplex should use is specified with option lpalg.

Solving linear programming problems is memory intensive. Even though Cplex manages memory veryefficiently, insufficient physical memory is one of the most common problems when running large LPs.When memory is limited, Cplex will automatically make adjustments which may negatively impactperformance. If you are working with large models, study the section entitled Physical MemoryLimitations carefully.

Cplex is designed to solve the majority of LP problems using default option settings. These settingsusually provide the best overall problem optimization speed and reliability. However, there areoccasionally reasons for changing option settings to improve performance, avoid numerical difficulties,control optimization run duration, or control output options.

Some problems solve faster with the dual simplex algorithm rather than the default primal simplexalgorithm. In particular, highly degenerate problems with little variability in the right-hand-sidecoefficients but significant variability in the cost coefficients often solve much faster using dual simplex.Also, very few problems exhibit poor numerical performance in both the primal and the dual. Therefore,consider trying dual simplex if numerical problems occur while using primal simplex.

Cplex has a very efficient algorithm for network models. Network constraints have the followingproperty:

each non-zero coefficient is either a +1 or a -1 each column appearing in these constraints has exactly 2 nonzero entries, one with a +1 coefficientand one with a -1 coefficient

Cplex can also automatically extract networks that do not adhere to the above conventions as long as

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they can be transformed to have those properties.

The barrier algorithm is an alternative to the simplex method for solving linear programs. It employs aprimal-dual logarithmic barrier algorithm which generates a sequence of strictly positive primal and dualsolutions. Specifying the barrier algorithm may be advantageous for large, sparse problems.

GAMS/Cplex also provides access to the Cplex Infeasibility Finder. The Infeasibility finder takes aninfeasible linear program and produces an irreducibly inconsistent set of constraints (IIS). An IIS is a setof constraints and variable bounds which is infeasible but becomes feasible if any one member of the setis dropped. GAMS/Cplex reports the IIS in terms of GAMS equation and variable names and includesthe IIS report as part of the normal solution listing.

3.2 Mixed-Integer Programming

The methods used to solve pure integer and mixed integer programming problems require dramaticallymore mathematical computation than those for similarly sized pure linear programs. Many relativelysmall integer programming models take enormous amounts of time to solve.

For problems with integer variables, Cplex uses a branch and bound algorithm (with cuts) which solvesa series of LP subproblems. Because a single mixed integer problem generates many LP subproblems,even small mixed integer problems can be very compute intensive and require significant amounts ofphysical memory.

4 GAMS Options

The following GAMS options are used by GAMS/Cplex:

Option IterLim = n; Sets the iteration limit. The algorithm will terminate and pass on thecurrent solution to GAMS. In case a pre-solve is done, the post-solveroutine will be invoked before reporting the solution.

Cplex handles the iteration limit for MIP problems differently than someother GAMS solvers. For MIP problems, controlling the length of thesolution run by limiting the execution time (ResLim) is preferable.

IterLim is not used at all when solving an LP with the barrier algorithm.By default, Cplex will use an iteration limit based on problemcharacteristics. To specify a barrier iteration limit, use Cplex parameterbaritlim.

OPTION ResLim = x; Sets the time limit in seconds. The algorithm will terminate and pass onthe current solution to GAMS. In case a pre-solve is done, the post-solveroutine will be invoked before reporting the solution.

Option OptCA = x; Absolute optimality criterion for a MIP problem.

Option OptCR = x ; Relative optimality criterion for a MIP problem. Notice that Cplex uses aslightly different definition than GAMS normally uses. The OptCR optionsignals Cplex to stop when

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(|BP-BF|)/(1.0+|BP|) < OptCR

where BF is the objective function value of the current best integersolution while BP is the best possible integer solution. The GAMSdefinition is:

(|BP-BF|)/(|BP|) < OptCR

Option Bratio = x; Determines whether or not to use an advanced basis. A value of 1.0causes GAMS to instruct Cplex not to use an advanced basis. A value of0.0 causes GAMS to construct a basis from whatever information isavailable. The default value of 0.25 will nearly always cause GAMS topass along an advanced basis if a solve statement has previously beenexecuted. Note that using an advanced basis will disable the Cplexpresolve. Especially for mixed integer problems, the presolve may bemore beneficial than an advanced basis.

Option SysOut = On; Will echo Cplex messages to the GAMS listing file. This option may beuseful in case of a solver failure.

ModelName.OptFile = 1; Instructs Cplex to read the option file. The name of the option file iscplex.opt.

ModelName.Cheat = x; Cheat value: each new integer solution must be at least x better than theprevious one. Can speed up the search, but you may miss the optimalsolution. The cheat parameter is specified in absolute terms (like theOptCA option). The Cplex option objdif overrides the GAMS cheatparameter.

ModelName.Cutoff = x; Cutoff value. When the branch and bound search starts, the parts of thetree with an objective worse than x are deleted. This can sometimes speedup the initial phase of the branch and bound algorithm.

ModelName.PriorOpt = 1; Instructs Cplex to use priority branching information passed by GAMSthrough the variable.prior parameters.

ModelName.TryInt = x; Causes GAMS/Cplex to make use of current variable values when solvinga MIP problem. If a variable value is within x of a bound, it will bemoved to the bound and the preferred branching direction for that variablewill be set toward the bound. Moving the value to the bound allows thevariable to be part of an initial integer solution when using parametermipstart. The preferred branching direction will only be effective whenpriorities are used.

5 Summary of Cplex Options

The various Cplex options are listed here by category, with a few words about each to indicate itsfunction. The options are listed again, in alphabetical order and with detailed descriptions, in the lastsection of this document.

5.1 Preprocessing and General Options

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advind advanced basis useaggfill aggregator fill parameteraggind aggregator on/offcoeredind coefficient reduction on/offdepind dependency checker on/offlpalg algorithm to use for solving an LP predual give dual problem to the optimizerpreind turn presolver on/offprepass number of presolve applications to performprintoptions write values of all options to the GAMS listing filerelaxpreind presolve for initial relaxation on/offrerun rerun problem if presolve infeasible or unboundedscaind matrix scaling on/offtilim overrides the GAMS ResLim option

5.2 Simplex Algorithmic Options

craind crash strategy (used to obtain starting basis)dpriind dual simplex pricingepper perturbation constantiis run the IIS finder if the problem is infeasibleiisind IIS finder method to usenetfind attempt network extractionnetfinishalg algorithm to finish solution with after solving the networkperind force initial perturbationperlim number of stalled iterations before perturbationppriind primal simplex pricingpricelim pricing candidate listreinv refactorization frequencysimthreads number of threads for parallel dual simplex algorithm

5.3 Simplex Limit Options

itlim iteration limitobjllim objective function lower limitobjulim objective function upper limitsinglim limit on singularity repairs

5.4 Simplex Tolerance Options

epmrk Markowitz pivot toleranceepopt optimality tolerance

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eprhs feasibility tolerance

5.5 Barrier Specific Options

baralg algorithm selectionbarcolnz dense column handlingbarepcomp convergence tolerancebargrowth unbounded face detectionbaritlim iteration limitbarmaxcor maximum correction limitbarobjrng maximum objective functionbarorder row ordering algorithm selectionbarthreads number of threads for parallel barrier algorithmbarvarup variable upper limitcrossoveralg crossover method

5.6 MIP Algorithmic Options

bbinterval best bound intervalbndstrenind bound strengtheningbrdir set branching directionbttol backtracking limitcliques clique cut generationcovers cover cut generationcutlo lower cutoff for tree searchcutsfactor cut limitcutup upper cutoff for tree searchflowcovers flow cover cut generationgubcovers GUB cover cut generationheurfreq heuristic frequencyheuristic initial integer solution heuristicimplbd implied bound cut generationmiphybalg crossover type when solving subproblems with barriermipordind priority list on/offmipordtype priority order generationmipstart use mip starting valuesmipthreads number of threads for parallel mip algorithmnodefiledir node storage directorynodefileind node storage file indicatornodefilelim node file size limitnodelim maximum number of nodes to process

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nodesel node selection strategyprobe perform probing before solving a MIPstartalg algorithm for initial LPstrongcandlim size of the candidates list for strong branchingstrongitlim limit on iterations per branch for strong branchingstrongthreadlim number of threads for strong branchingsubalg algorithm for subproblemsvarsel variable selection strategy at each node

5.7 MIP Limit Options

intsollim maximum number of integer solutionsnodelim maximum number of nodes to solvetrelim maximum space in memory for tree

5.8 MIP Tolerance Options

epagap absolute stopping toleranceepgap relative stopping toleranceepint integrality toleranceobjdif over-rides GAMS Cheat parameterrelobjdif relative cheat parameter

5.9 Output Options

bardisplay progress display levelmipdisplay progress display levelmipinterval progress display intervalnetdisplay network display levelsimdisplay simplex display levelwritebas produce a Cplex basis filewritemps produce a Cplex MPS filewritelp produce a Cplex LP filewritesav produce a Cplex binary problem filewritesos produce a Cplex sos file

5.10 The GAMS/Cplex Options File

The GAMS/Cplex options file consists of one option or comment per line. An asterisk (*) at thebeginning of a line causes the entire line to be ignored. Otherwise, the line will be interpreted as anoption name and value separated by any amount of white space (blanks or tabs). Anything after the valuewill be ignored.

Following is an example options file cplex.opt.

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lpalg dual simdisplay 2

It will cause Cplex to solve an LP with the dual simplex algorithm instead of the default primalalgorithm. The iteration log will have an entry for each iteration instead of an entry for eachrefactorization.

6 Special Notes

6.1 Physical Memory Limitations

For the sake of computational speed, Cplex should use only available physical memory rather thanvirtual or paged memory. When Cplex recognizes that a limited amount of memory is available itautomatically makes algorithmic adjustments to compensate. These adjustments almost always reduceoptimization speed. Learning to recognize when these automatic adjustments occur can help todetermine when additional memory should be added to the computer.

On virtual memory systems, if memory paging to disk is observed, a considerable performance penalty isincurred. Increasing available memory will speed the solution process dramatically.

Cplex performs an operation called refactorization at a frequency determined by the reinv option setting.The longer Cplex works between refactorizations, the greater the amount of memory required tocomplete each iteration. Therefore, one means for conserving memory is to increase the refactorizationfrequency. Since refactorizing is an expensive operation, increasing the refactorization frequency byreducing the reinv option setting generally will slow performance. Cplex will automatically increase therefactorization frequency if it encounters low memory availability. This can be seen by watching theiteration log. The default log reports problem status at every refactorization. If the number of iterationsbetween iteration log entries is decreasing, Cplex is increasing the refactorization frequency. SinceCplex might increase the frequency to once per iteration, the impact on performance can be dramatic.Providing additional memory should be beneficial.

6.2 Using Special Ordered Sets

For some models a special structure can be exploited. GAMS allows you to declare SOS1 and SOS2variables (Special Ordered Sets of type 1 and 2).

In Cplex the definition for SOS1 variables is:

A set of variables for which at most one variable may be non-zero.

The definition for SOS2 variables is:

A set of variables for which at most two variables may be non-zero. If two variables arenon-zero, they must be adjacent in the set.

6.3 Running Out of Memory for MIP problems

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The most common difficulty when solving MIP problems is running out of memory. This problem ariseswhen the branch and bound tree becomes so large that insufficient memory is available to solve an LPsubproblem. As memory gets tight, you may observe frequent warning messages while Cplex attempts tonavigate through various operations within limited memory. If a solution is not found shortly thesolution process will be terminated with an unrecoverable integer failure message.

The tree information saved in memory can be substantial. Cplex saves a basis for every unexplored node.When utilizing the best bound method of node selection, the list of such nodes can become very long forlarge or difficult problems. How large the unexplored node list can become is entirely dependent on theactual amount of physical memory available and the actual size of the problem. Certainly increasing theamount of memory available extends the problem solving capability. Unfortunately, once a problem hasfailed because of insufficient memory, you can neither project how much further the process needed togo nor how much memory would be required to ultimately solve it.

Memory requirements can be limitted by using the trelim option with the nodefileind option. Settingnodefileind to 2 or 3 will cause Cplex to store portions of the branch and bound tree on disk whenever itgrows to larger than the size specified by option trelim. That size should be set to something less thanthe amount of physical memory available.

Another approach is to modify the solution process to utilize less memory.

Set option bttol to a number closer to 1.0 to cause more of a depth-first-search of the tree. Set option nodesel to use a best estimate strategy or, more drastically a depth-first-search. Depthfirst search rarely generates a large unexplored node list since Cplex will be diving deep into thebranch and bound tree rather than jumping around within it. Set option varsel to use strong branching. Strong branching spends extra computation time at eachnode to choose a better branching variable. As a result it generates a smaller tree. It is often fasteroverall, as well. On some problems, a large number of cuts will be generated without a correspondingly largebenefit in solution speed. Cut generation can be turned off using options cliques, covers,flowcovers, gubcovers, and implbd.

6.4 Failing to Prove Integer Optimality

One frustrating aspect of the branch and bound technique for solving MIP problems is that the solutionprocess can continue long after the best solution has been found. Remember that the branch and boundtree may be as large as 2n nodes, where n equals the number of binary variables. A problem containingonly 30 binary variables could produce a tree having over one billion nodes! If no other stopping criteriahave been set, the process might continue ad infinitum until the search is complete or your computer'smemory is exhausted.

In general you should set at least one limit on the optimization process before beginning an optimization.Setting limits ensures that an exhaustive tree search will terminate in reasonable time. Once terminated,you can rerun the problem using some different option settings. Consider some of the shortcutsdescribed previously for improving performance including setting the options for mip gap, objectivevalue difference, upper cutoff, or lower cutoff.

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7 GAMS/Cplex Log file

Cplex reports its progress by writing to the GAMS log file as the problem solves. Normally the GAMSlog file is directed to the computer screen.

The log file shows statistics about the presolve and continues with an iteration log.

For the primal simplex algorithm, the iteration log starts with the iteration number followed by thescaled infeasibility value. Once feasibility has been attained, the objective function value is listedinstead. At the default value for option simdisplay there is a log line for each refactorization. The screenlog has the following appearance:

Tried aggregator 1 time.LP Presolve eliminated 2 rows and 39 columns.Aggregator did 30 substitutions.Reduced LP has 243 rows, 335 columns, and 3912 nonzeros.Presolve time = 0.01 sec.Using conservative initial basis.

Iteration log . . .Iteration: 1 Scaled infeas = 193998.067174Iteration: 29 Objective = -3484.286415Switched to devex.Iteration: 98 Objective = -1852.931117Iteration: 166 Objective = -349.706562

Optimal solution found.

Objective : 901.161538

The iteration log for the dual simplex algorithm is similar, but the dual infeasibility and dual objectiveare reported instead of the corresponding primal values:

Tried aggregator 1 time.LP Presolve eliminated 2 rows and 39 columns.Aggregator did 30 substitutions.Reduced LP has 243 rows, 335 columns, and 3912 nonzeros.Presolve time = 0.01 sec.

Iteration log . . .Iteration: 1 Scaled dual infeas = 3.890823Iteration: 53 Dual objective = 4844.392441Iteration: 114 Dual objective = 1794.360714Iteration: 176 Dual objective = 1120.183325Iteration: 238 Dual objective = 915.143030Removing shift (1).



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The log for the network algorithm adds statistics about the extracted network and a log of the networkiterations. The optimization is finished by one of the simplex algorithms and an iteration log for that isproduced as well.

Tried aggregator 1 time.LP Presolve eliminated 2 rows and 39 columns.Aggregator did 30 substitutions.Reduced LP has 243 rows, 335 columns, and 3912 nonzeros.Presolve time = 0.01 sec.Extracted network with 25 nodes and 116 arcs.Extraction time = -0.00 sec.Iteration log . . .Iteration: 0 Infeasibility = 1232.378800 (-1.32326e+12)

Network - Optimal: Objective = 1.5716820779e+03Network time = 0.01 sec. Iterations = 26 (24)

Iteration log . . .Iteration: 1 Scaled infeas = 212696.154729Iteration: 62 Scaled infeas = 10020.401232Iteration: 142 Scaled infeas = 4985.200129Switched to devex.Iteration: 217 Objective = -3883.782587Iteration: 291 Objective = -1423.126582



The log for the barrier algorithm adds various algorithm specific statistics about the problem beforestarting the iteration log. The iteration log includes columns for primal and dual objective values andinfeasibility values. A special log follows for the crossover to a basic solution.

Tried aggregator 1 time.LP Presolve eliminated 2 rows and 39 columns.Aggregator did 30 substitutions.Reduced LP has 243 rows, 335 columns, and 3912 nonzeros.Presolve time = 0.02 sec.Number of nonzeros in lower triangle of A*A' = 6545Using Approximate Minimum Degree orderingTotal time for automatic ordering = 0.01 sec.Summary statistics for Cholesky factor: Rows in Factor = 243 Integer space required = 578 Total non-zeros in factor = 8491 Total FP ops to factor = 410889 Itn Primal Obj Dual Obj Prim Inf Upper Inf Dual Inf 0 -1.2826603e+06 7.4700787e+08 2.25e+10 6.13e+06 4.00e+05 1 -2.6426195e+05 6.3552653e+08 4.58e+09 1.25e+06 1.35e+05 2 -9.9117854e+04 4.1669756e+08 1.66e+09 4.52e+05 3.93e+04 3 -2.6624468e+04 2.1507018e+08 3.80e+08 1.04e+05 1.20e+04 4 -1.2104334e+04 7.8532364e+07 9.69e+07 2.65e+04 2.52e+03

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5 -9.5217661e+03 4.2663811e+07 2.81e+07 7.67e+03 9.92e+02 6 -8.6929410e+03 1.4134077e+07 4.94e+06 1.35e+03 2.16e+02 7 -8.3726267e+03 3.1619431e+06 3.13e-07 6.84e-12 3.72e+01 8 -8.2962559e+03 3.3985844e+03 1.43e-08 5.60e-12 3.98e-02 9 -3.8181279e+03 2.6166059e+03 1.58e-08 9.37e-12 2.50e-02 10 -5.1366439e+03 2.8102021e+03 3.90e-06 7.34e-12 1.78e-02 11 -1.9771576e+03 1.5960442e+03 3.43e-06 7.02e-12 3.81e-03 12 -4.3346261e+02 8.3443795e+02 4.99e-07 1.22e-11 7.93e-04 13 1.2882968e+02 5.2138155e+02 2.22e-07 1.45e-11 8.72e-04 14 5.0418542e+02 5.3676806e+02 1.45e-07 1.26e-11 7.93e-04 15 2.4951043e+02 6.5911879e+02 1.73e-07 1.43e-11 5.33e-04 16 2.4666057e+02 7.6179064e+02 7.83e-06 2.17e-11 3.15e-04 17 4.6820025e+02 8.1319322e+02 4.75e-06 1.78e-11 2.57e-04 18 5.6081604e+02 7.9608915e+02 3.09e-06 1.98e-11 2.89e-04 19 6.4517294e+02 7.7729659e+02 1.61e-06 1.27e-11 3.29e-04 20 7.9603053e+02 7.8584631e+02 5.91e-07 1.91e-11 3.00e-04 21 8.5871436e+02 8.0198336e+02 1.32e-07 1.46e-11 2.57e-04 22 8.8146686e+02 8.1244367e+02 1.46e-07 1.84e-11 2.29e-04 23 8.8327998e+02 8.3544569e+02 1.44e-07 1.96e-11 1.71e-04 24 8.8595062e+02 8.4926550e+02 1.30e-07 2.85e-11 1.35e-04 25 8.9780584e+02 8.6318712e+02 1.60e-07 1.08e-11 9.89e-05 26 8.9940069e+02 8.9108502e+02 1.78e-07 1.07e-11 2.62e-05 27 8.9979049e+02 8.9138752e+02 5.14e-07 1.88e-11 2.54e-05 28 8.9979401e+02 8.9139850e+02 5.13e-07 2.18e-11 2.54e-05 29 9.0067378e+02 8.9385969e+02 2.45e-07 1.46e-11 1.90e-05 30 9.0112149e+02 8.9746581e+02 2.12e-07 1.71e-11 9.61e-06 31 9.0113610e+02 8.9837069e+02 2.11e-07 1.31e-11 7.40e-06 32 9.0113661e+02 8.9982723e+02 1.90e-07 2.12e-11 3.53e-06 33 9.0115644e+02 9.0088083e+02 2.92e-07 1.27e-11 7.35e-07 34 9.0116131e+02 9.0116262e+02 3.07e-07 1.81e-11 3.13e-09 35 9.0116154e+02 9.0116154e+02 4.85e-07 1.69e-11 9.72e-13Barrier time = 0.39 sec.

Primal crossover. Primal: Fixing 13 variables. 12 PMoves: Infeasibility 1.97677059e-06 Objective 9.01161542e+02 0 PMoves: Infeasibility 0.00000000e+00 Objective 9.01161540e+02 Primal: Pushed 1, exchanged 12. Dual: Fixing 3 variables. 2 DMoves: Infeasibility 1.28422758e-36 Objective 9.01161540e+02 0 DMoves: Infeasibility 1.28422758e-36 Objective 9.01161540e+02 Dual: Pushed 3, exchanged 0.Using devex.Total crossover time = 0.02 sec.



For MIP problems, during the branch and bound search, Cplex reports the node number, the number ofnodes left, the value of the Objective function, the number of integer variables that have fractionalvalues, the current best integer solution, the best relaxed solution at a node and an iteration count. Thelast column show the current optimality gap as a percentage.

Tried aggregator 1 time.MIP Presolve eliminated 1 rows and 1 columns.Reduced MIP has 99 rows, 76 columns, and 419 nonzeros.Presolve time = 0.00 sec.

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Iteration log . . .Iteration: 1 Dual objective = 0.000000Root relaxation solution time = 0.01 sec.

Nodes Cuts/ Node Left Objective IInf Best Integer Best Node ItCnt Gap

0 0 0.0000 24 0.0000 40* 0+ 0 6.0000 0 6.0000 0.0000 40 100.00%* 50+ 50 4.0000 0 4.0000 0.0000 691 100.00% 100 99 2.0000 15 4.0000 0.4000 1448 90.00%Fixing integer variables, and solving final LP..Tried aggregator 1 time.LP Presolve eliminated 100 rows and 77 columns.All rows and columns eliminated.Presolve time = 0.00 sec.

Solution satisfies tolerances.

MIP Solution : 4.000000 (2650 iterations, 185 nodes)Final LP : 4.000000 (0 iterations)

Best integer solution possible : 1.000000Absolute gap : 3Relative gap : 1.5

8 Detailed Descriptions of Cplex Options

These options should be entered in the options file after setting the GAMS ModelName.OptFileparameter to 1. The name of the option file is 'cplex.opt'. The option file is case sensitive and thekeywords should be given in full.

advind (integer)

Use an Advanced Basis. GAMS/Cplex will automatically use an advanced basis from a previous solvestatement. The GAMS Bratio option can be used to specify when not to use an advanced basis. TheCplex option advind can be used to ignore a basis passed on by GAMS (it overrides the Bratio option).Note that using an advanced basis will disable the Cplex presolve. (default is determined by GAMS Bratio)

0 do not use advanced basis1 use advanced basis if available

aggfill (integer)

Aggregator fill limit. If the net result of a single substitution is more non-zeros than the setting of theAGGFILL parameter, the substitution will not be made. (default = 10)

aggind (integer)

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This option, when set to a nonzero value, will cause the Cplex aggregator to use substitution wherepossible to reduce the number of rows and columns in the problem. If set to a positive value, theaggregator will be applied the specified number of times, or until no more reductions are possible. At thedefault value of -1, the aggregator is applied once for linear programs and an unlimited number of timesfor mixed integer problems. (default = -1)

-1 once for LP, unlimited for MIP0 do not use

baralg (integer)

Selects which barrier algorithm to use. The default setting of 0 uses the infeasibility-estimate startalgorithm for MIP subproblems and the standard barrier algorithm, option 3, for other cases. Thestandard barrier algorithm is almost always fastest. The alternative algorithms, options 1 and 2, mayeliminate numerical difficulties related to infeasibility, but will generally be slower. (default = 0)

0 Same as 1 for MIP subproblems, 3 otherwise1 Infeasibility-estimate start2 Infeasibility-constant start2 standard barrier algorithm

barcolnz (integer)

Determines whether or not columns are considered dense for special barrier algorithm handling. At thedefault setting of 0, this parameter is determined dynamically. Values above 0 specify the number ofentries in columns to be considered as dense. (default = 0)

bardisplay (integer)

Determines the level of progress information to be displayed while the barrier method is running. (default = 1)

0 No progress information1 Display normal information2 Display diagnostic information

barepcomp (real)

Determines the tolerance on complementarity for convergence of the barrier algorithm. The algorithmwill terminate with an optimal solution if the relative complementarity is smaller than this value. (default = 1.0 e-8)

bargrowth (real)

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Used by the barrier algorithm to detect unbounded optimal faces. At higher values, the barrier algorithmwill be less likely to conclude that the problem has an unbounded optimal face, but more likely to havenumerical difficulties if the problem does have an unbounded face. (default = 1.0 e6)

baritlim (integer)

Determines the maximum number of iterations for the barrier algorithm. The default value of -1 allowsCplex to automatically determine the limit based on problem characteristics. (default = -1)

barmaxcor (integer)

Specifies the maximum number of centering corrections that should be done on each iteration. Largervalues may improve the numerical performance of the barrier algorithm at the expense of computationtime. The default of -1 means the number is automatically determined. Range - [-1, 10] (default = -1)

barobjrng (real)

Determines the maximum absolute value of the objective function. The barrier algorithm looks at thislimit to detect unbounded problems. (default = 1.0 e20)

barorder (integer)

Determines the ordering algorithm to be used by the barrier method. By default, Cplex attempts tochoose the most effective of the available alternatives. Higher numbers tend to favor better orderings atthe expense of longer ordering runtimes. (default = 0)

0 Automatic1 Approximate Minimum Degree (AMD)2 Approximate Minimum Fill (AMF)3 Nested Dissection (ND)

barstartalg (integer)

This option sets the algorithm to be used to compute the initial starting point for the barrier solver. Thedefault starting point is satisfactory for most problems. Since the default starting point is tuned forprimal problems, using the other starting points may be worthwhile in conjunction with the predualparameter. (default = 1)

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1 default primal, dual is 02 default primal, estimate dual3 primal average, dual is 04 primal average, estimate dual

barthreads (integer)

This option sets a limit on the number of threads available to the parallel barrier algorithm. The actualnumber of processors used will be the minimum of this number and the number of available processors.The parallel option is separately licensed. (default = the number of threads licensed)

barvarup (real)

Determines an upper bound for all variables that have no finite upper bound. This number is used by thebarrier algorithm to detect unbounded optimal faces. (default = 1.0 e20)

bbinterval (integer)

Set interval for selecting a best bound node when doing a best estimate search. Active only when nodeselis 2 (best estimate). Decreasing this interval may be useful when best estimate is finding good solutionsbut making little progress in moving the bound. Increasing this interval may help when the best estimatenode selection is not finding any good integer solutions. Setting the interval to 1 is equivalent to settingnodesel to 1. (default = 7)

bndstrenind (integer)

Use bound strengthening when solving mixed integer problems. Bound strengthening tightens thebounds on variables, perhaps to the point where the variable can be fixed and thus removed fromconsideration during the branch and bound algorithm. This reduction is usually beneficial, butoccasionaly, due to its iterative nature, takes a long time. (default = -1)

-1 Determine automatically0 Don't use bound strengthening1 Use bound strengthening

brdir (integer)

Used to decide which branch (up or down) should be taken first at each node. (default = 0)

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-1 Down branch selected first0 Algorithm decides1 Up branch selected first

bttol (real)

This option controls how often backtracking is done during the branching process. At each node, Cplexcompares the objective function value or estimated integer objective value to these values at parentnodes; the value of the bttol parameter dictates how much relative degradation is tolerated beforebacktracking. Lower values tend to increase the amount of backtracking, making the search more of apure best-bound search. Higher values tend to decrease the amount of backtracking, making the searchmore of a depth-first search. This parameter is used only once a first integer solution is found or when acutoff has been specified. Range: [0.0, 1.0] (default = 0.01)

cliques (integer)

Determines whether or not clique cuts should be generated during optimization. (default = 0)

-1 Do not generate clique cuts0 Determined automatically1 Generate clique cuts at the root node only2 Generate clique cuts throughout the tree

coeredind (integer)

Coefficient reduction is a technique used when presolving mixed integer programs. The benefit is toimprove the objective value of the initial (and subsequent) linear programming relaxations by reducingthe number of non-integral vertices. However, the linear programs generated at each node may becomemore difficult to solve. (default = 2)

0 Do not use coefficient reduction1 Reduce only to integral coefficients2 Reduce all potential coefficients

covers (integer)

Determines whether or not cover cuts should be generated during optimization. (default = 0)

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-1 Do not generate cover cuts0 Determined automatically1 Generate cover cuts at the root node only2 Generate cover cuts throughout the tree

craind (integer)

The crash option biases the way Cplex orders variables relative to the objective function when selectingan initial basis. (default = 1)

Primal: 0 ignore objective coefficients during crash-1, 1 alternate ways of using objective coefficients

Dual: 0, -1 aggressive starting basis1 default starting basis

crossoveralg (string)

Specifies the method to be used for creating a basis after solving with the barrier algorithm. (default = primal)

primal primal crossoverdual dual crossovernone no crossover; solution is non-basic

cutlo (real)

Lower cutoff value for tree search in maximization problems. This is used to cut off any nodes that havean objective value below the lower cutoff value. This option overrides the GAMS Cutoff setting. A toorestrictive value may result in no integer solutions being found. (default = -1 e +75)

cutsfactor (real)

This option limits the number of cuts that can be added. The number of rows in the problem with cutsadded is limited to cutsfactor times the original (after presolve) number of rows. (default = 4.0)

cutup (real)

Upper Cutoff value for tree search in minimization problems. This is used to cut off any nodes that havean objective value above the upper cutoff value. This option overrides the GAMS Cutoff setting. A toorestrictive value may result in no integer solutions being found.

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(default = 1 e +75)

depind (integer)

This option determines if the dependency checker will be used. (default = 0)

0 Do not use the dependency checker1 Use the dependency checker

dpriind (integer)

Pricing strategy for dual simplex method. Consider using dual steepest-edge pricing. Dual steepest-edgeis particularly efficient and does not carry as much computational burden as the primal steepest-edgepricing. (default = 0)

0 Determined automatically1 Standard dual pricing2 Steepest-edge pricing3 Steepest-edge pricing in slack space4 Steepest-edge pricing, unit initial norms

epagap (real)

Absolute tolerance on the gap between the best integer objective and the objective of the best noderemaining. When the value falls below the value of the epagap setting, the optimization is stopped. Thisoption overrides GAMS OptCA which provides its initial value. (default = GAMS OptCA)

epgap (real)

Relative tolerance on the gap between the best integer objective and the objective of the best noderemaining. When the value falls below the value of the epgap setting, the mixed integer optimization isstopped. Note the difference in the Cplex definition of the relative tolerance with the GAMS definition.This option overrides GAMS OptCR which provides its initial value. Range: [0.0, 1.0] (default = GAMS OptCR)

epint (real)

Integrality Tolerance. This specifies the amount by which an integer variable can be different than aninteger and still be considered feasible. Range: [1.0e-9, 1.0] (default = 1.0e-5)

epmrk (real)

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The Markowitz tolerance influences pivot selection during basis factorization. Increasing the Markowitzthreshold may improve the numerical properties of the solution. Range - [0.0001, 0.99999] (default = 0.01)

epopt (real)

The optimality tolerance influences the reduced-cost tolerance for optimality. This option setting governshow closely Cplex must approach the theoretically optimal solution. Range - [1.0e-9, 1.0e-4] (default = 1.0e-6)

epper (integer)

Perturbation setting. Highly degenerate problems tend to stall optimization progress. Cplexautomatically perturbs the variable bounds when this occurs. Perturbation expands the bounds on everyvariable by a small amount thereby creating a different but closely related problem. Generally, thesolution to the less constrained problem is easier to solve. Once the solution to the perturbed problemhas advanced as far as it can go, Cplex removes the perturbation by resetting the bounds to their originalvalues.

If the problem is perturbed more than once, the perturbation constant is probably too large. Reduce theepper option to a level where only one perturbation is required. Any non-negative values are valid. (default = 1.0e-6)

eprhs (real)

Feasibility tolerance. This specifies the degree to which a problem's basic variables may violate theirbounds. This tolerance influences the selection of an optimal basis and can be reset to a lower valuewhen a problem is having difficulty maintaining feasibility during optimization. You may also wish tolower this tolerance after finding an optimal solution if there is any doubt that the solution is trulyoptimal. If the feasibility tolerance is set too low, Cplex may falsely conclude that a problem isinfeasible. Range - [1.0e-9, 1.0e-4] (default = 1.0e-6)

flowcovers (integer)

Determines whether or not flow cover cuts should be generated during optimization. (default = 0)

-1 Do not generate flow cover cuts0 Determined automatically1 Generate flow cover cuts at the root node only2 Generate flow cover cuts throughout the tree

gubcovers (integer)

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Determines whether or not GUB (Generalized Upper Bound) cover cuts should be generated duringoptimization. (default = 0)

-1 Do not generate GUB cover cuts0 Determined automatically1 Generate GUB cover cuts at the root node only2 Generate GUB cover cuts throughout the tree

heurfreq (integer)

This option specifies how often to apply the node heuristic. (default = 0)

-1 Do not use the node heuristic0 Determined automatically

heuristic (integer)

Affects the use of heuristics in finding an initial integer feasible solution. (default = -1)

-1 Do not use heuristics0 Determined automatically1 Use a heuristic at node 0

iis (string)

Find an IIS (Irreducably Inconsistent Set of constraints) and write an IIS report to the GAMS solutionlisting if the model is found to be infeasible. Legal option values are yes or no. (default = yes)

iisind (integer)

This option specifies which method to use for finding an IIS (Irreducably Inconsistent Set of constraints).Note that the iis option must be set to cause an IIS computation. This option just specifies the method. (default = 0)

0 Method with minimum computation time1 Method generating minimum size for the IIS

implbd (integer)

Determines whether or not implied bound cuts should be generated during optimization. (default = 0)

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-1 Do not generate implied bound cuts0 Determined automatically1 Generate implied bound cuts at the root node only2 Generate implied bound cuts throughout the tree

intsollim (integer)

This option limits the MIP optimization to finding only this number of mixed integer solutions beforestopping. (default = 2,100,000,000)

itlim (integer)

The iteration limit option sets the maximum number of iterations before the algorithm terminates,without reaching optimality. This Cplex option overrides the GAMS IterLim option. Any non-negativeinteger value is valid. (default = GAMS IterLim)

lpalg (string)

The algorithm to use when solving an LP. (default = primal)

primal primal simplexdual dual simplexnetwork network algorithm followed by primal or dual simplex as specified by option netfinishalg. barrier barrier algorithm with optional crossover to a basic solution as specified by option

crossoveralg.

mipdisplay (integer)

The amount of information displayed during MIP solution increases with increasing values of thisoption. (default = 4)

0 No display1 Display integer feasible solutions.2 Displays nodes under mipinterval control.3 Same as 2 but adds information on cuts. 4 Same as 3 but adds LP display for the root node.5 Same as 3 but adds LP display for all nodes.

miphybalg (integer)

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Determines the type of crossover to use when the barrier algorithm is used for solving MIP subproblems.

(default = 1)

1 primal2 dual

mipinterval (integer)

The MIP interval option value determines the frequency of the node logging when the mipdisplay optionis set to 2 or 3. If the value of the MIP interval option setting is n, then only every nth node, plus allinteger feasible nodes, will be logged. This option is useful for selectively limiting the volume ofinformation generated for a problem that requires many nodes to solve. Any non-negative integer valueis valid. (default = 100)

mipordind (integer)

Use priorities. Priorities should be assigned based on your knowledge of the problem. Variables withhigher priorities will be branched upon before variables of lower priorities. This direction of the treesearch can often dramatically reduce the number of nodes searched. For example, consider a problemwith a binary variable representing a yes/no decision to build a factory, and other binary variablesrepresenting equipment selections within that factory. You would naturally want to explore whether ornot the factory should be built before considering what specific equipment to purchased within thefactory. By assigning a higher priority to the build/nobuild decision variable, you can force this logic intothe tree search and eliminate wasted computation time exploring uninteresting portions of the tree. Whenset at 0 (default), the mipordind option instructs Cplex not to use priorities for branching. When set to 1,priority orders are utilized.

Note: Priorities are assigned to discrete variables using the .prior suffix in the GAMS model. Lower.prior values mean higher priority. The .prioropt model suffix has to be used to signal GAMS to exportthe priorities to the solver. (default = 1)

0 Do not use priorities for branching1 Priority orders are utilized

mipordtype (integer)

This option is used to select the type of generic priority order to generate when no priority order ispresent. (default = 0) 0 None1 decreasing cost magnitude2 increasing bound range3 increasing cost per coefficient count

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mipstart (integer)

This option controls the use of advanced starting values for mixed integer programs. A setting of 1indicates that the values should be checked to see if they provide an integer feasible solution beforestarting optimization. (default = 0)

0 do not use the values1 use the values

mipthreads (integer)

This option sets a limit on the number of threads available to the parallel mip algorithm. The actualnumber of processors used will be the minimum of this number and the number of available processors.The parallel option is separately licensed. (default = the number of threads licensed)

netdisplay (integer)

This option controls the log for network iterations. (default = 2)

0 No network log.1 Displays true objective values.2 Displays penalized objective values.

netfind (integer)

Specifies the level of network extraction to be done. Note that option lpalg must be used to cause thenetwork algorithm to execute. This option only specifies the level of network extraction. (default = 2)

0 Cplex automatically determines the level of network extraction to be done.1 Extract pure network only2 Try reflection scaling3 Try general scaling

netfinishalg (string)

When using the network algorithm, either the primal or dual simplex method can be used to finish thesolution. (default = primal)

primal Use primal simplex.dual Use dual simplex.

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nodefiledir (string)

Directory for use in storing node files. This parameter can be overridden by using an environmentvariable (TMPDIR under Unix and TMP under Windows 95 or NT). (default = GAMS scratch directory)

nodefileind (integer)

Node files are used when the tree memory limit (option trelim) is reached. At the default value of 0,optimization is terminated if trelim is reached. Otherwise a group of nodes is removed from memory andtransferred to a node file. The group of nodes is returned to memory as needed. Using node files is amuch better option than using swap space. (default = 0)

0 No node files1 Node files in memory and compressed2 Node files on disk3 Node files on disk and compressed

nodefilelim (real)

The maximum amount of disk space the node files can consume before the optimization is terminated. (default = 1.0e+75)

nodelim (integer)

The maximum number of nodes solved before the algorithm terminates, without reaching optimality.

nodesel (integer)

This option is used to set the rule for selecting the next node to process when backtracking. (default = 1)

0 Depth-first search. This chooses the most recently created node.1 Best-bound search. This chooses the unprocessed node with the best objective function for the

associated LP relaxation.2 Best-estimate search. This chooses the node with the best estimate of the integer objective value that

would be obtained once all integer infeasibilities are removed.3 Alternate best-estimate search.

objdif (real)

A means for automatically updating the cutoff to more restrictive values. Normally the most recentlyfound integer feasible solution objective value is used as the cutoff for subsequent nodes. When thisoption is set to a positive value, the value will be subtracted from (added to) the newly found integerobjective value when minimizing (maximizing). This forces the MIP optimization to ignore integersolutions that are not at least this amount better than the one found so far. The option can be adjusted to

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improve problem solving efficiency by limiting the number of nodes; however, setting this option at avalue other than zero (the default) can cause some integer solutions, including the true integer optimum,to be missed. Negative values for this option will result in some integer solutions that are worse than orthe same as those previously generated, but will not necessarily result in the generation of all possibleinteger solutions. This option overrides the GAMS Cheat parameter. (default = 0.0)

objllim (real)

Setting a lower objective function limit will cause Cplex to halt the optimization process once theminimum objective function value limit has been exceeded. (default = -1.0e+75)

objulim (real)

Setting an upper objective function limit will cause Cplex to halt the optimization process once themaximum objective function value limit has been exceeded. (default = 1.0e+75)

perind (integer)

Perturbation Indicator. If a problem automatically perturbs early in the solution process, consider startingthe solution process with a perturbation by setting perind to 1. Manually perturbing the problem willsave the time of first allowing the optimization to stall before activating the perturbation mechanism, butis useful only rarely, for extremely degenerate problems. (default = 0)

0 not automatically perturbed1 automatically perturbed

perlim (integer)

Perturbation limit. The number of stalled iterations before perturbation is invoked. The default value of 0means the number is determined automatically. (default = 0)

ppriind (integer)

Pricing algorithm. Likely to show the biggest impact on performance. Look at overall solution time andthe number of Phase I and total iterations as a guide in selecting alternate pricing algorithms. If you areusing the dual Simplex method use dpriind to select a pricing algorithm. If the number of iterationsrequired to solve your problem is approximately the same as the number of rows in your problem, thenyou are doing well. Iteration counts more than three times greater than the number of rows suggest thatimprovements might be possible. (default = 0)

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-1 Reduced-cost pricing. This is less compute intensive and may be preferred if the problem is small oreasy. This option may also be advantageous for dense problems (say 20 to 30 nonzeros per column).

0 Hybrid reduced-cost and Devex pricing.1 Devex pricing. This may be useful for more difficult problems which take many iterations to

complete Phase I. Each iteration may consume more time, but the reduced number of total iterationsmay lead to an overall reduction in time. Tenfold iteration count reductions leading to threefoldspeed improvements have been observed. Do not use devex pricing if the problem has manycolumns and relatively few rows. The number of calculations required per iteration will usually bedisadvantageous.

2 Steepest edge pricing. If devex pricing helps, this option may be beneficial. Steepest-edge pricing iscomputationally expensive, but may produce the best results on exceptionally difficult problems.

3 Steepest edge pricing with slack initial norms. This reduces the computationally intensive nature ofsteepest edge pricing.

4 Full pricing

predual (integer)

Solve the dual. Some linear programs with many more rows than columns may be solved faster byexplicitly solving the dual. The predual option will cause Cplex to solve the dual while returning thesolution in the context of the original problem. This option is ignored if presolve is turned off. (default = 0)

0 do not give dual to optimizer1 give dual to optimizer

preind (integer)

Perform Presolve. This helps most problems by simplifying, reducing and eliminating redundancies.However, if there are no redundancies or opportunities for simplification in the model, if may be fasterto turn presolve off to avoid this step. On rare occasions, the presolved model, although smaller, may bemore difficult than the original problem. In this case turning the presolve off leads to better performance.Specifying 0 turns the aggregator off as well. (default = 1)

0 Do not presolve the problem1 Perform the presolve

prepass (integer)

Number of MIP presolve applications to perform. By default, Cplex determines this automatically.Specifying 0 turns off the presolve but not the aggregator. Set preind to 0 to turn both off. (default = -1)

-1 Determined automatically0 No presolve

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pricelim (integer)

Size for the pricing candidate list. Cplex dynamically determines a good value based on problemdimensions. Only very rarely will setting this option manually improve performance. Any non-negativeintege values are valid. (default = 0, in which case it is determined automatically)

printoptions (string)

Write the values of all options to the GAMS listing file. Valid values are no or yes. (default = no)

probe (integer)

Determines the amount of probing performed on a MIP. Probing can be both very powerful and verytime consuming. Setting the value to 1 can result in dramatic reductions or dramatic increases in solutiontime depending on the particular model. (default = 0)

-1 no probing0 automatic1 full probing

reinv (integer)

Refactorization Frequency. This option determines the number of iterations between refactorizations ofthe basis matrix. The default should be optimal for most problems. Cplex's performance is relativelyinsensitive to changes in refactorization frequency. Only for extremely large, difficult problems shouldreducing the number of iterations between refactorizations be considered. Any non-negative integervalue is valid. (default = 0, in which case it is determined automatically)

relaxpreind (integer)

This option will cause the Cplex presolve to be invoked for the initial relaxation of a mixed integerprogram (according to the other presolve option settings). Sometimes, additional reductions can be madebeyond any MIP presolve reductions that may already have been done. (default = 0)

0 do not presolve initial relaxation1 use presolve on initial relaxation

relobjdif (real)

The relative version of the objdif option. Ignored if objdif is non-zero. (default = 0)

rerun (string)

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The Cplex presolve can sometimes diagnose a problem as being infeasible or unbounded. When thishappens, GAMS/Cplex can, in order to get better diagnostic information, rerun the problem with thepresolve and aggregator turned off. The GAMS solution listing will then mark variables and equations asinfeasible or unbounded according to the final solution returned by the simplex algorithm. The iis optioncan be used to get even more diagnostic information. The rerun option controls this behavior. Validvalues are yes or no. (default = yes)

scaind (integer)

This option influences the scaling of the problem matrix. (default = 0)

-1 No scaling0 Standard scaling - An equilibration scaling method is implemented which is generally very effective.1 Modified, more aggressive scaling method that can produce improvements on some problems. This

scaling should be used if the problem is observed to have difficulty staying feasible during thesolution process.

simdisplay (integer)

This option controls what Cplex reports (normally to the screen) during optimization. The amount ofinformation displayed increases as the setting value increases. (default = 1)

0 No iteration messages are issued until the optimal solution is reported.1 An iteration log message will be issued after each refactorization. Each entry will contain the

iteration count and scaled infeasibility or objective value.2 An iteration log message will be issued after each iteration. The variables, slacks and artificials

entering and leaving the basis will also be reported.

simthreads (integer)

This option sets a limit on the number of threads available to the parallel simplex algorithm. The actualnumber of processors used will be the minimum of this number and the number of available processors.The parallel option is separately licensed. (default = the number of threads licensed)

singlim (integer)

The singularity limit setting restricts the number of times Cplex will attempt to repair the basis whensingularities are encountered. Once the limit is exceeded, Cplex replaces the current basis with the bestfactorizable basis that has been found. Any non-negative integer value is valid. (default = 10)

startalg (integer)

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Selects the algorithm to use for the initial relaxation of a MIP. (default = 2)

1 primal simplex2 dual simplex3 network followed by dual simplex4 barrier with crossover5 dual simplex to iteration limit, then barrier6 barrier without crossover

strongcandlim (integer)

Limit on the length of the candidate list for strong branching (varsel = 3). (default = 10)

strongitlim (integer)

Limit on the number of iterations per branch in strong branching (varsel = 3). The default value of 0causes the limit to be chosen automatically which is normally satisfactory. Try reducing this value if thetime per node seems excessive. Try increasing this value if the time per node is reasonable but Cplex ismaking little progress. (default = 0)

strongthreadlim (integer)

Controls the number of parallel threads available for strong branching (varsel = 3). This option doesnothing if option mipthreads is greater than 1. (default = 1)

subalg (integer)

Strategy for solving linear sub-problems at each node. (default = 2)

1 primal simplex2 dual simplex3 network optimizer followed by dual simplex4 barrier with crossover5 dual simplex to iteration limit, then barrier6 barrier without crossover

tilim (real)

The time limit setting determines the amount of time in seconds that Cplex will continue to solve aproblem. This Cplex option overrides the GAMS ResLim option. Any non-negative value is valid. (default = GAMS ResLim)

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trelim (real)

Maximum amount of space in megabytes that the branch and bound tree can consume in memory. Anypositive value is valid. Consider allocating half of a machine's memory to the tree to a maximum ofabout 32 megabytes. Larger values provide only marginal improvement relative to the use of node files(option nodefilind). (default = 1.0e+75)

varsel (integer)

This option is used to set the rule for selecting the branching variable at the node which has beenselected for branching. The default value of 0 allows Cplex to select the best rule based on the problemand its progress. (default = 0)

-1 Branch on variable with minimum infeasibility. This rule may lead more quickly to a first integerfeasible solution, but will usually be slower overall to reach the optimal integer solution.

0 Branch variable automatically selected.1 Branch on variable with maximum infeasibility. This rule forces larger changes earlier in the tree,

which tends to produce faster overall times to reach the optimal integer solution.2 Branch based on pseudo costs. Generally, the pseudo-cost setting is more effective when the

problem contains complex trade-offs and the dual values have an economic interpretation.3 Strong Branching. This setting causes variable selection based on partially solving a number of

subproblems with tentative branches to see which branch is most promising. This is often effectiveon large, difficult problems.

4 Branch based on pseudo reduced costs.

writebas (character string)

Write a basis file.

writelp (character string)

Write a file in Cplex LP format.

writemps (character string)

Write an MPS problem file.

writesav (character string)

Write a binary problem file.

writesos (character string)

Write a file containing the SOS structure. For models with SOS variables only.

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Appendix: Cplex Licensing

The Cplex library, upon which GAMS/Cplex is based, uses a license manager to control use of thelibrary. This is in addition to the normal GAMS license.

GAMS/Cplex licensing operations are performed using a set of commands described below. Using thesecommands (instead of running the Cplex licensing utility directly) will ensure that the license is set up ina standard way. This will avoid potential conflicts with other products and will make support easier ifproblems do arise.

The same set of licensing commands is used on PCs and Unix machines. The differences in how theyoperate are described in the next couple of sections. For both platforms, the command files reside in thecplex subdirectory of the GAMS system directory. When they are run, though, the current directoryshould be the GAMS system directory. The commands therefore look like cplex\update on a PC, orcplex/update on a Unix machine.

PC Licensing

If no evidence is seen of another Cplex license, a GAMS/Cplex demonstration license will be installedautomatically at GAMS installation (gamsinst) time.

A GAMS/Cplex license is installed in a standard location. This location depends on the machineconfiguration and the operating system that is being used, but that information is determinedautomatically. An environment variable, CPLEXLICDIR, will be automatically defined whenever one ofthe licensing commands is being run as well as when GAMS/Cplex is actually solving a model.CPLEXLICDIR should not be set manually unless requested by GAMS support.

Unix Licensing

A GAMS/Cplex demonstration license is not automatically installed on Unix systems. That is because,under Unix, there is no good choice for a standard location for the license. The location must bespecified by the person doing the installation.

The location for the license is specified by creating a license pointer file that contains the absolute pathof a license directory. The pointer file must be created in a standard place and the directory that it pointsto should not exist yet (it will be created by the Cplex licensing utility). The location must be the cplexsubdiretory of the GAMS system directory and its name should be cplexlicfile. The suggested name forthe directory is cplexlicdir, but that is not enforced.

The location for cplexlicdir should be picked so that it will not have to be moved. Moving it, or evensaving and restoring it from tape, will corrupt the Cplex license. It should not be kept under, or in asubdirectory of, the GAMS system directory. Otherwise, you may inadvertently delete the license whenupgrading to a new version of GAMS.

An environment variable, CPLEXLICENSE, will be automatically defined whenever one of thelicensing commands is being run as well as when GAMS/Cplex is actually solving a model.CPLEXLICENSE should not be set manually unless requested by GAMS support.

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Licensing Commands

A Cplex license is installed and maintained by running a few commands. These commands automatedefining the required environment variables, provide some GAMS specific checks, and call the Cplexlicense utility. All commands should be run from the command prompt. The current directory must bethe GAMS system directory.

The command descriptions that follow call for using e-mail to [email protected]. Please include thecontents of both gamslice.txt and cpxlicen.log (PC) or cpxlicense.log (Unix) in the body of your e-mail.This is the preferred method to avoid transcription errors and to provide an electronic record. If e-mailcannot be used, a fax is the second choice. Transcribing license codes by phone can be done if necessary.

instdemo

Installs a demonstration license. GAMS/Cplex will work but will enforce size restrictions. This can beinstalled for testing purposes even if an unrestricted license will be installed later. In that case, however,the demonstration license will have to be be deleted with delete before getting an initial license code byrunning initial.

initial

Produces an initial license code that should be sent to [email protected]. A copy of the filegamslice.txt from the GAMS system directory should be included with the e-mail to avoid confusionabout which GAMS system is being licensed. This requires that a GAMS/Cplex license does not alreadyexist on the machine.

update

Installs a permanent (or evaluation) license. The code is obtained from GAMS by first running initialand sending the initial license code to [email protected]. GAMS/Cplex should be operational afterrunning update.

delete

Deletes both the GAMS/Cplex license and the license directory. This is usually only required if ademonstration license is to be replaced with an evaluation or permanent license.

transfer

Deletes the GAMS/Cplex license and produces a license code to be taken to another machine. Requiresan initial license code from the other machine as input.

restore

Restores a corrupted license. This should be run only when requested by GAMS support. It requires arestore code as input.

Installing a New License

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The following steps must be followed to license a new GAMS/Cplex system.

1. If a demo license has been installed, it should be deleted using the delete command. 2. On Unix machines, the license directory pointer file, cplexlicfile, must be created. See Unix

Licensing above. 3. An initial Cplex license code should be obtained by running the initial command. 4. Copies of gamslice.txt and cpxlicen.log (cpxlicense.log for Unix) should be e-mailed to

[email protected] with a request for a permanent or an evaluation license code. 5. A new license code will be returned by e-mail -- usually later the same day. It should be installed by

running the update command.

Updating an Existing License

The method used to install an update depends on how the old license was installed.

PC Using cplex\update

If the existing license was installed using cplex\update, it can be updated the same way. [email protected] for an update code. Don't forget to include the contents of gamslice.txt andcpxlicen.log (cpxlicense.log for Unix).

The cplex\update command can be used to find out if the existing license was installed the same way. Ifit was, output similar to the following will be seen:

A CPLEX license already exists on this machine.The current license code is: 30-C5-4E-F4-1C-EB-D5-4A-99-39-92-09-DC-BE

Environment variable settings: CPLEXLICDIR='C:\WINDOWS\gmscplic'

Type 'quit' or enter a license in the form: XX-XX-XX-XX-XX-XX-XX-XX-XX-XX-XX-XX-XX-XX

Otherwise a message will be seen that says "No CPLEX license was found on this machine..."

PC Using CPLEXLICTYPE=DEFAULT

Some existing GAMS/Cplex systems were licensed by putting

SET CPLEXLICTYPE=DEFAULT

in the autoexec.bat file and running cpxlicen directly. For those systems, set up the new license using theinitial and update commands. After the new license is working, delete the old license by 1) making surethe CPLEXLICDIR environment variable is not set and 2) running cpxlicen -d. You will need to sendthe delete confirmation code to [email protected].

To test if the existing license was installed with CPLEXLICTYPE=DEFAULT, simply type SET at thecommand prompt to see if CPLEXLICTYPE is defined.

PC Using a Hardware Key

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Cplex no longer supports licensing with a hardware key. To switch to keyless licensing, set up the newlicense using the initial and update commands. You will need to return the key to GAMS Developmentafter testing the new license.

Unix

Simply make cplex/cplexlicfile point to the existing license and ask [email protected] for a code touse with the update command.

Last modified April 28, 1999 by PES

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gams/cplex 6.5 user notes

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